Estimation of coastal waters turbidity using Sentinel-2 imagery
Abstract
Turbidity is an important water quality parameter and an indicator of water pollution. Marine remote sensing techniques has become a useful tool for mapping of turbidity at coastal waters. The advantage of using remote sensing for water quality analysis is its ability to obtain synoptic data from the entire study area to produce continuous surface data, can shows detailed spatial variability and periodically. The empirical modeling has been applied in this study to formulate the mathematical relationship between coastal waters turbidity with Sentinel-2 reflectance. This study integrated field survey and image processing. Measurement of in-situ turbidity was done in accordance with imagery acquisition time. Imageries used for this study were Sentinel-2 level-2A. The mathematical relationship was obtained by multiple linear regression model between turbidity and Sentinel-2 reflectance. A mathematical model has been developed in Sentinel-2 imagery and successfully applied to obtain surface turbidity. Estimated turbidity derived from Sentinel-2 imagery is very close to observed turbidity so the proposed model can be used to retrieve turbidity of coastal waters.
Keyword : coastal waters, turbidity, Sentinel-2, reflectance, empirical modeling, multiple linear regression
How to Cite
Amran, M. A., & Daming, W. S. (2023). Estimation of coastal waters turbidity using Sentinel-2 imagery. Geodesy and Cartography, 49(4), 180–185. https://doi.org/10.3846/gac.2023.18132
This work is licensed under a Creative Commons Attribution 4.0 International License.
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Caballero, I., Stumpf, R. P., & Meredith, A. (2019). Preliminary assessment of turbidity and chlorophyll impact on bathymetry derived from Sentinel-2A and Sentinel-3A satellites in South Florida. Remote Sensing, 11(6), 645. https://doi.org/10.3390/rs11060645
Dogliotti, A. I., Ruddick, K. G., Nechad, B., Doxaran, D., & Knaeps, E. (2015). A single algorithm to retrieve turbidity from remotely-sensed data in all coastal and estuarine waters. Remote Sensing of Environment, 156, 157–168. https://doi.org/10.1016/j.rse.2014.09.020
Doxaran, D., Froidefond, J. M., Castaing, P., & Babin, M. (2009). Dynamics of the turbidity maximum zone in a macrotidal estuary (the Gironde, France): Observations from field and MODIS satellite data. Estuarine, Coastal and Shelf Science, 81(3), 321–332. https://doi.org/10.1016/j.ecss.2008.11.013
Gernez, P., Barille, L., Lerouxel, A., Mazeran, C., Lucas, A., & Doxaran, D. (2014). Remote sensing of suspended particulate matter in turbid oyster-farming ecosystems. Journal of Geophysical Research: Oceans, 119(10), 7277–7294. https://doi.org/10.1002/2014JC010055
Güttler, F. N., Niculescu, S., & Gohin, F. (2013). Turbidity retrieval and monitoring of Danube Delta waters using multi-sensor optical remote sensing data: An integrated view from the delta plain lakes to the western–northwestern Black Sea coastal zone. Remote Sensing of Environment, 132(15), 86–101. https://doi.org/10.1016/j.rse.2013.01.009
Hu, Z., Pan, D., He, X., & Bai, Y. (2016). Diurnal variability of turbidity fronts observed by geostationary satellite ocean color remote sensing. Remote Sensing, 8(2), 147–162. https://doi.org/10.3390/rs8020147
Islam, M. A., Lan-Wei, W., Smith, C. J., Reddy, S., Lewis, A., & Smith, A. (2007). Evaluation of satellite remote sensing for operational monitoring of sediment plumes produced by dredging at hay point, Queensland, Australia. Journal of Applied Remote Sensing, 1(1), 011506–011521. https://doi.org/10.1117/1.2834768
Katlane, R., Dupouy, C., El Kilani, B., & Berges, J. C. (2020). Estimation of chlorophyll and turbidity using Sentinel 2A and EO1 data in Kneiss Archipelago Gulf of Gabes, Tunisia. International Journal of Geosciences, 11, 708–728. https://doi.org/10.4236/ijg.2020.1110035
Lihan, T., Saitoh, S. I., Iida, T., Hirawake, T., & Iida, K. (2008). Satellite-measured temporal and spatial variability of the Tokachi River plume. Estuarine Coastal and Shelf Science, 78(2), 237–249. https://doi.org/10.1016/j.ecss.2007.12.001
Liu, Y., Islam, M. A., & Gao, J. (2003). Quantification of shallow water quality parameters by means of remote sensing. Progress in Physical Geography, 27(1), 24–43. https://doi.org/10.1191/0309133303pp357ra
Miller, P. I., Xu, W., & Carruthers, M. (2015). Seasonal shelf-sea front mapping using satellite ocean colour and temperature to support development of a marine protected area network. Deep-Sea Researh Part II-Topical Study in Oceanography, 119, 3–19. https://doi.org/10.1016/j.dsr2.2014.05.013
Obregón, M. A., Rodrigues, G., Costa, M. J., Potes, M., & Silva, A. M. (2019). Validation of ESA Sentinel-2 L2A aerosol optical thickness and columnar water vapour during 2017–2018. Remote Sensing, 11(14), 1649. https://doi.org/10.3390/rs11141649
Ouillon, S., Douillet, P., & Andrefouet, S. (2004). Coupling satellite data with in situ measurements and numerical modeling to study fine suspended-sediment transport: A study for the lagoon of New Caledonia. Coral Reefs, 23(1), 109–122. https://doi.org/10.1007/s00338-003-0352-z
Ouillon, S., Douillet, P., Petrenko, A., Neveux, J., Dupouy, C., Froidefond, J. M., Andréfouët, S., & Caravaca, A. M. (2008). Optical algorithms at satellite wavelengths for total suspended matter in tropical coastal waters. Sensors, 8(7), 4165–4185. https://doi.org/10.3390/s8074165
Ouma, Y. O., Noor, K., & Herbert, K. (2020). Modelling reservoir chlorophyll-a, TSS, and turbidity using Sentinel-2A MSI and Landsat-8 OLI satellite sensors with empirical multivariate regression. Journal of Sensors, 2020, 8858408. https://doi.org/10.1155/2020/8858408
Petus, C., Chust, G., Gohin, F., Doxaran, D., Froidefond, J. M., & Sagarminaga, Y. (2010). Estimating turbidity and total suspended matter in the Adour River plume (South Bay of Biscay) using MODIS 250-m imagery. Continental Shelf Research, 30(5), 379–392. https://doi.org/10.1016/j.csr.2009.12.007
Quang, N. H., Sasaki, J., Higa, H., & Huan, N. H. (2017). Spatiotemporal variation of turbidity based on Landsat 8 OLI in Cam Ranh Bay and Thuy Trieu Lagoon, Vietnam. Water, 9(8), 570–594. https://doi.org/10.3390/w9080570
Ray, R., Mandal, S., & Dhara, A. (2013). Environmental monitoring of estuaries: Estimating and mapping various environmental indicators in Matla estuarine complex, using Landsat TM digital data. International Journal of Geomatics and Geosciences, 3(3), 570–581.
Sebastiá-Frasquet, M. T., Aguilar-Maldonado, J. A., Santamaría-Del-Ángel, E., & Estornell, J. (2019). Sentinel 2 analysis of turbidity patterns in a coastal lagoon. Remote Sensing, 11(24), 2926. https://doi.org/10.3390/rs11242926
Sravanthi, N., Ramana, I. V., Yunus Ali, P., Ashraf, M., Ali, M. M., & Narayana, A. C. (2013). An algorithm for estimating suspended sediment concentrations in the coastal waters of India using remotely sensed reflectance and its application to coastal environments. International Journal of Environmental Research, 7(4), 841–850.
Vanhellemont, Q., & Ruddick, K. (2014). Turbid wakes associated with offshore wind turbines observed with Landsat 8. Remote Sensing of Environment, 145, 105–115. https://doi.org/10.1016/j.rse.2014.01.009
Wu, G., Cui, L., Liu, L., Chen, F., Fei, T., & Liu, Y. (2015). Statistical model development and estimation of suspended particulate matter concentrations with Landsat 8 OLI images of Dongting Lake, China. International Journal of Remote Sensing, 36(1), 343–360. https://doi.org/10.1080/01431161.2014.995273
Zhang, Y., Zhang, Y., Shi, K., Zha, Y., Zhou, Y., & Liu, M. (2016). A Landsat 8 OLI-based, semianalytical model for estimating the total suspended matter concentration in the slightly turbid Xin’anjiang reservoir (China). IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(1), 1–16. https://doi.org/10.1109/JSTARS.2015.2509469